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Nonlinear dynamical modeling of neural activity using volterra series with GA-enhanced particle swarm optimization algorithm

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Abstract

In order to improve the modeling performance of Volterra sequence for nonlinear neural activity, in this paper, a new optimization algorithm is proposed to identify Volterra sequence parameters. Algorithm combines the advantages of particle swarm optimization (PSO) and genetic algorithm (GA) improve the performance of the identification of nonlinear model parameters from rapidity and accuracy. In the modeling experiments of neural signal data generated by the neural computing model and clinical neural data set in this paper, the proposed algorithm shows its excellent potential in nonlinear neural activity modeling. Compared with PSO and GA, the algorithm can achieve less identification error, and better balance the convergence speed and identification error. Further, we explore the influence of algorithm parameters on identification efficiency, which provides possible guiding significance for parameter setting in practical application of the algorithm.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant Number: 62173241), the Natural Science Foundation of Tianjin, China (Grant Number: 20JCQNJC01160), the Foundation of Tianjin University (Grant Number: 2020XRG-0018). The authors also gratefully acknowledge the financial support provided by Opening Foundation of Key Laboratory of Opto-technology and Intelligent Control (Lanzhou Jiaotong University), Ministry of Education (KFKT2020-01).

Funding

National Natural Science Foundation of China,62173241,Chen Liu,Natural Science Foundation of Tianjin City,20JCQNJC01160,Chen Liu, Foundation of Tianjin University, 2020XRG-0018,Chen Liu, Opening Foundation of Key Laboratory of Opto-technology and Intelligent Control (Lanzhou Jiaotong University), Ministry of Education, KFKT2020-01, Chen Liu

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, 30072, China

    Siyuan Chang, Jiang Wang, Yulin Zhu, Xile Wei, Bin Deng & Chen Liu

  2. School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, China

    Huiyan Li

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  1. Siyuan Chang
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  2. Jiang Wang
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  3. Yulin Zhu
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  4. Xile Wei
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  5. Bin Deng
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  6. Huiyan Li
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  7. Chen Liu
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Correspondence to Chen Liu.

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Chang, S., Wang, J., Zhu, Y. et al. Nonlinear dynamical modeling of neural activity using volterra series with GA-enhanced particle swarm optimization algorithm. Cogn Neurodyn 17, 467–476 (2023). https://doi.org/10.1007/s11571-022-09822-1

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  • DOI: https://doi.org/10.1007/s11571-022-09822-1

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